ACP plus plus : Action Co-Occurrence Priors for Human-Object Interaction Detection

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A common problem in the task of human-object interaction (HOI) detection is that numerous HOI classes have only a small number of labeled examples, resulting in training sets with a long-tailed distribution. The lack of positive labels can lead to low classification accuracy for these classes. Towards addressing this issue, we observe that there exist natural correlations and anti-correlations among human-object interactions. In this paper, we model the correlations as action co-occurrence matrices and present techniques to learn these priors and leverage them for more effective training, especially on rare classes. The efficacy of our approach is demonstrated experimentally, where the performance of our approach consistently improves over the state-of-the-art methods on both of the two leading HOI detection benchmark datasets, HICO-Det and V-COCO.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON IMAGE PROCESSING, v.30, pp.9150 - 9163

ISSN
1057-7149
DOI
10.1109/TIP.2021.3113563
URI
http://hdl.handle.net/10203/289388
Appears in Collection
EE-Journal Papers(저널논문)
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